Model of human preattentive visual detection of edge orientation anomalies

Abstract

Psychophysical studies provide evidence of preattentive visual processing characterized by parallel operations performed on a limited set of features. Since these operations extend well beyond the foveal or high-resolution area of the visual field, one may assume that they are based on lower-resolution features. Such parallelism and data reduction imply computationally efficient processing that could be emulated for machine vision pattern recognition purposes. Several models of preattentive texture segmentation have recently been presented in the computational vision literature. This paper presents a model of human preattentive visual detection of pattern anomalies. Operating on a low-frequency, band-pass filtered image, the model detects singularities by comparing local to global statistics of contrast and edge orientation. The model has been applied to simple schematic images. It successfully predicts the asymmetry in search latencies whereby a target characterized by a preattentively detectable feature 'pops out' of a field of distractors not containing the feature, but when target and distractors are switched, serial search is required to locate the 'odd man out.' The model has also been shown to detect pattern defects on periodic, multilevel integrated circuits.